Furthermore, processing such high-dimensional data is a somewhat complex task due to an increased number of parameters. This is known as the curse of dimensionality which considerably influences the classification performance especially in the case of supervised learning [1]}. Since the size of training data is not adequate/insufficient and/or not reliable (i.e. the training samples may not provide any new information to the model or may have similar patterns/structures) to properly train the classifier which may lead the model to overfit. This is known as the Hughes phenomena [2]} which occurs when labeled training data is significantly smaller than the number of spectral bands present in the data. Lack of labeled HSI data is a major issue in HSIC as labeling of HSI is a time-consuming and expensive task because it usually requires human experts or investigation of real-time scenarios.
